In the MRI technologies, the imaging speed is a very important parameter. Several hours were generally required for an examination in early stages of the technology, and since then the imaging speed has been increased quite significantly owning to the technical improvements in relation to field intensity, gradient hardware and pulse sequences. However, fast changes of field gradient and high density continuous radio frequency (RF) pulses would result in a specific absorption rate (SAR) and the amount of heat generated in organs and tissues which have become unbearably beyond human physiological limits, therefore increasing the imaging speed has met a bottleneck.
Thereafter, researchers found that the speed of magnetic resonance imaging could be greatly increased by virtue of the application of complicated computer aided image reconstruction algorithms together with cooperated coil array, and such a technology was commonly referred to as parallel imaging technology. The reconstruction of parallel-acquired images is a technology for image reconstruction using parallel acquisition, which utilizes the differences in the spatial sensitivities between phase-controlled arrayed coils to perform spatial encoding and utilizes the phase-controlled arrayed coils to acquire data simultaneously, so that when compared with the imaging speed of conventional MRI it obtained an imaging speed of 2 to 6 times higher or even more. By adopting the parallel imaging technology, it has brought forward new requirements to MRI systems; for example, there are needs for multiple receiving channels, multi-arrayed coils and calibration of the sensitivities of the coils, the use of special data processing and image reconstructing methods and so on.
Parallel imaging can increase image acquisition speed and the increase of imaging speed is achieved by reducing the filling rate in K-space. However, if the filling rate in K-space is below the limit by Nyquist theorem, it would lead to the appearance of artifacts in the images reconstructed using direct Fourier reconstruction. The images of common MRI are obtained by acquiring an object's information in frequency domain which is subject to Fourier transformation. According to the Nyquist theorem, an object's repeating cycle in the image domain is in inverse proportion to the sampling interval in the frequency domain. If an image's spatial repeat cycle is smaller than the size of the image itself, the reconstructed images will be superposed, and this phenomenon is referred to in signal processing as overlapping.
As to multi-coil acquisition, although the K-space information acquired by each coil is not sufficient, the differences between the signals acquired by different coils can be utilized and processed to obtain a complete image. The reconstruction algorithms for eliminating overlapped artifacts for parallel imaging can be divided roughly into two categories: the simultaneous acquisition of spatial harmonics technique (SMASH) and the sensitivity encoding parallel acquisition technique (SENSE). Among them, the SMASH method is a method using the sensitivity functions of various channel coils to form spatial harmonics and to perform assistant encoding. The sensitivity function of an ordinary coil is of slow variation and can be regarded as a Gaussian distribution function, and then a linear combination of the sensitivity functions of various channel coils can be used to form spatial harmonics of a certain frequency. And the spatial harmonic function is used to make up a phase encoding line whose data are not actually acquired. Griswold et al. made further improvements to SMASH, and they proposed the generalized autocalibrating partially parallel acquisition (GRAPPA) algorithm. Differing from the fitting by SMASH of the K-space under-sampling data into a final image of full sampling, GRAPPA fits the under-sampled K-space data of each channel. GRAPPA has improved the precision of the reconstructed images while demanding more processing time. Currently, it has been proposed a scheme that taking the reference lines into account in the GRAPPA algorithm, that is to use the low-frequency full-sampled data to substitute the corresponding fitted data when fitting the under-sampled K-space data, so as to improve the SNR of the reconstructed images.
Differing from the processing scheme of SMASH in the frequency domain, the SENSE method removes the artifacts generated by under-sampling by means of solving a linear equation set in the image domain. Because of the overlapping effects due to the spatial cycle of the image, the SENSE method performs full-sampling in the central region of K-space during the data acquisition, and performs under-sampling in the peripheral regions. The raw data of K-space are then divided into two portions: the uniform under-sampled data and low frequency full-sampled data. The uniform under-sampled data are used to produce an overlapped image, while the low frequency full-sampled data are used to produce a blurred image of tissues and further to obtain a real-time sensitivity distribution and weighted matrix of the coils, and finally by synthesizing the overlapped image produced by using of the under-sampled data and the weighted matrix obtained by using of the low frequency full-sampled data, an image of high resolution without overlapping is obtained. Here, the low frequency full-sampled data for obtaining the sensitivity distribution of the coils and the weighted matrix are referred to as reference data, and the phase encoding lines in K-space for low frequency full-sampling are referred to as reference lines. In the present applicant's Chinese patent titled “the method and device for parallel acquisition and image reconstruction”, it is described a scheme for performing reconstruction by taking the reference lines into account of the SENSE algorithm, that is to say the reconstruction data are generated according to a hybrid sampling mode to contain the combination of the under-sampled data in K-space and the low-frequency full-sampled data, and the hybrid sampling mode is taken in account during the image reconstruction.
In the present applicant's Chinese patent application no. 200410082376.8, entitled “image reconstruction algorithm for fast and generalized autocalibrating parallel acquisition in MRI imaging”, it is described a generalized evaluation algorithm for SNR loss, which can be used in the parallel imaging methods both in image space and in K-space. However, in the evaluation algorithm for SNR loss described above, the contribution of the reference lines to the SNR is not considered.